Dynamic Fuel Tankering

ABSTRACT

Disclosed herein are methods and systems for dynamically calculating a total fuel uplift quantity for an aircraft scheduled to fly a flight route. In one aspect, a method comprises: (a) polling a plurality of sources to receive data indicative of: (i) real-time weather conditions in remaining flight sectors in the flight route, and (ii) delay information in the remaining sectors; (b) calculating for the remaining sectors a respective fuel consumption factor; (c) based on (i) respective fuel quotations in the remaining sectors, (ii) the real-time weather conditions, and (iii) the delay information, generating a linear model for calculating a respective fuel uplift quantity at arrival stations in the remaining sectors; (d) calculating using the linear model the respective fuel uplift quantity at the arrival stations; and (e) periodically performing operations (a)-(d) to update a calculation of the respective fuel uplift quantities to account for changing factors.

FIELD

The present disclosure generally relates to computing systems andmethods for dynamic fuel tankering.

BACKGROUND

Aircraft fuel consumption is one of the highest operating costs forairlines. Given this, many airlines attempt to decrease costs byimplementing various measures to decrease the cost of aircraft fuelconsumption. For example, airlines may attempt to increase aircraftconsumption efficiency.

Another measure utilized by airlines to reduce fuel costs is fueltankering (also referred to as “economic fueling”). This measurecapitalizes on fuel price variations at different airports. Generally,the approach involves an aircraft, flying between a departure airportand an arrival airport, carrying extra fuel from the departure airportthat has lower fuel prices than the arrival airport. By carrying theextra lower costing fuel, the airline may reduce costs by decreasing thequantity of the more expensive fuel that is refueled at the arrivalstation.

SUMMARY

One aspect of the disclosure is method for dynamically calculating atotal fuel uplift quantity for an aircraft to account for changingfactors as the aircraft flies along a flight route that comprises aplurality of flight sectors, where a flight sector comprises arespective flight between a respective departure station and arespective arrival station. The method involves (a) polling in real-timea plurality of sources to receive data indicative of: (i) real-timerespective weather conditions in each of one or more remaining flightsectors in the flight route, and (ii) respective delay information forthe respective flights in the remaining flight sectors. Additionally,the method involves (b) calculating for the remaining flight sectors arespective fuel consumption factor, wherein the respective fuelconsumption factor is a function of altitude and travel distance thataccounts for variations in fuel consumption due to variations inaltitude and travel distance of the aircraft. The method also involves(c) based on (i) respective fuel quotations at the remaining arrivalstations, (ii) the real-time weather conditions, and (iii) therespective delay information for the respective flights in the remainingflight sectors, generating a linear model for calculating a respectivefuel uplift quantity at each remaining arrival station, where therespective fuel uplift quantity includes a respective quantity of fuelwith which the aircraft is refueled at each remaining arrival station.Further, the method involves (d) calculating using the linear model therespective fuel uplift quantity at each remaining arrival station,wherein the total fuel uplift quantity is a sum of the respective fueluplift quantities at the arrival stations in the flight route. Yetfurther, the method also involves (e) periodically performing operations(a)-(d) to update a calculation of the respective fuel upliftquantities.

Another aspect of the disclosure is a computing system for dynamicallycalculating a total fuel uplift quantity for an aircraft to account forchanging factors as the aircraft flies along a flight route thatcomprises a plurality of flight sectors, wherein a flight sectorcomprises a respective flight between a respective departure station anda respective arrival station, computing system including: a memory thatstores instruction, and a processor configured to execute theinstructions to perform operations. The operations include (a) pollingin real-time a plurality of sources to receive data indicative of: (i)real-time respective weather conditions in each of one or more remainingflight sectors in the flight route, and (ii) respective delayinformation for the respective flights in the remaining flight sectors.Additionally, the operations include (b) calculating for the remainingflight sectors a respective fuel consumption factor, where therespective fuel consumption factor is a function of altitude and traveldistance that accounts for variations in fuel consumption due tovariations in altitude and travel distance of the aircraft. Theoperations also include (c) based on (i) respective fuel quotations atthe remaining arrival stations, (ii) the real-time weather conditions,and (iii) the respective delay information for the respective flights inthe remaining flight sectors, generating a linear model for calculatinga respective fuel uplift quantity at each remaining arrival station,where the respective fuel uplift quantity includes a respective quantityof fuel with which the aircraft is refueled at each remaining arrivalstation. Further, the operations include (d) calculating using thelinear model the respective fuel uplift quantity at each remainingarrival station, wherein the total fuel uplift quantity is a sum of therespective fuel uplift quantities at the arrival stations in the flightroute. Yet further, the operations include (e) periodically performingoperations (a)-(d) to update a calculation of the respective fuel upliftquantities.

Another aspect of the disclosure is a non-transitory computer readablestorage medium having stored thereon program instructions that whenexecuted by a processor cause performance of a set of acts fordynamically calculating a total fuel uplift quantity for an aircraft toaccount for changing factors as the aircraft flies along a flight routethat comprises a plurality of flight sectors, where a flight sectorcomprises a respective flight between a respective departure station anda respective arrival station. The instructions include (a) polling inreal-time a plurality of sources to receive data indicative of: (i)real-time respective weather conditions in each of one or more remainingflight sectors in the flight route, and (ii) respective delayinformation for the respective flights in the remaining flight sectors.The instructions also include (b) calculating for the remaining flightsectors a respective fuel consumption factor, wherein the respectivefuel consumption factor is a function of altitude and travel distancethat accounts for variations in fuel consumption due to variations inaltitude and travel distance of the aircraft. Additionally, theinstructions include (c) based on (i) respective fuel quotations at theremaining arrival stations, (ii) the real-time weather conditions, and(iii) the respective delay information for the respective flights in theremaining flight sectors, generating a linear model for calculating arespective fuel uplift quantity at each remaining arrival station, wherethe respective fuel uplift quantity includes a respective quantity offuel with which the aircraft is refueled at each remaining arrivalstation. Further, the instructions include (d) calculating using thelinear model the respective fuel uplift quantity at each remainingarrival station, where the total fuel uplift quantity is a sum of therespective fuel uplift quantities at the arrival stations in the flightroute. Yet further, the instructions include (e) periodically performingoperations (a)-(d) to update a calculation of the respective fuel upliftquantities.

By the term “about” or “substantially” with reference to amounts ormeasurement values described herein, it is meant that the recitedcharacteristic, parameter, or value need not be achieved exactly, butthat deviations or variations, including for example, tolerances,measurement error, measurement accuracy limitations and other factorsknown to those of skill in the art, may occur in amounts that do notpreclude the effect the characteristic was intended to provide.

The features, functions, and advantages that have been discussed can beachieved independently in various examples or may be combined in yetother examples further details of which can be seen with reference tothe following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the illustrative examplesare set forth in the appended claims. The illustrative examples,however, as well as a preferred mode of use, further objectives anddescriptions thereof, will best be understood by reference to thefollowing detailed description of an illustrative example of the presentdisclosure when read in conjunction with the accompanying Figures.

FIG. 1 illustrates a flight route for an aircraft, according to anexample embodiment.

FIG. 2A illustrates a schematic diagram of a fuel tankering system,according to an example embodiment.

FIG. 2B is a representation of a linear model, according to an exampleembodiment.

FIG. 3 is a flowchart of a fuel tankering process, according to anexample embodiment.

FIG. 4 is a graphical user interface, according to an exampleembodiment.

FIG. 5 is a schematic diagram of a computing system, according to anexample embodiment.

FIG. 6A, 6B, 6C, 6D, 6E, 6F, and 6G each depict a block diagram of amethod, according to example embodiments.

DETAILED DESCRIPTION

Example methods, devices, and systems are described herein. It should beunderstood that the words “example” and “exemplary” are used herein tomean “serving as an example, instance, or illustration.” Any embodimentor feature described herein as being an “example” or “exemplary” is notnecessarily to be construed as preferred or advantageous over otherembodiments or features. Other embodiments can be utilized, and otherchanges can be made, without departing from the scope of the subjectmatter presented herein.

Thus, the example embodiments described herein are not meant to belimiting. Aspects of the present disclosure, as generally describedherein, and illustrated in the figures, can be arranged, substituted,combined, separated, and designed in a wide variety of differentconfigurations, all of which are contemplated herein.

Further, unless context suggests otherwise, the features illustrated ineach of the figures may be used in combination with one another. Thus,the figures should be generally viewed as component aspects of one ormore overall embodiments, with the understanding that not allillustrated features are necessary for each embodiment.

I. Overview

In line with the discussion above, an airline may implement a fueltankering procedure to reduce the fuel consumption costs of an aircraft.However, carrying extra fuel from a departure station to an arrivalstation results in extra weight, and thus additional fuel consumption bythe aircraft. Therefore, the airline may consider the transport cost ofthe extra fuel to determine whether to implement the fuel tankeringprocedure for the aircraft.

One way of analyzing the fuel transport cost is to calculate abreak-even price (P_(bep)) of the fuel at the arrival airport. Thebreak-even price represents the minimum fuel price at the arrivalairport at which the transportation of the extra fuel becomeseconomically feasible. The cost of transporting extra fuel from thedeparture station may depend on many factors, including but not limitedto, a length of the sector, the cruising altitude and speed of theaircraft flying the sector, and average wind speed.

Once the break-even price is calculated, the airline may compare thebreak-even price with the real price of fuel at the destination (P_(d))in order to determine whether to carry the extra fuel. If the break-evenprice is greater than the real price at the arrival airport, the airlinemay determine not to refuel the aircraft with the extra fuel. On theother hand, if the break-even price is less than the real price at thearrival airport, the airline may determine to refuel the aircraft withthe extra fuel.

Generally, the airline may implement this fuel tankering procedure on asector-by-sector basis for each of the airline's aircrafts. However,this procedure has many limitations that may prevent the airlines fromminimizing the fuel consumption costs for the aircrafts. For example,because this approach implements the fuel tankering procedure on asector-by-sector basis, the approach does not account for fuel pricevariations in more than one sector, thereby foregoing any reductions incost that may result from implementing the procedure along more than onesector. As another example, disruptions to airport operations (e.g.,weather conditions, lack of fuel supply, etc.) and the resulting delaycosts are not considered by the approach.

Disclosed herein are computing systems and methods that implement adynamic fuel tankering process that allows airlines to minimize fuelconsumption costs while accounting for constantly changing factors thatmay affect a flight. In particular, a fuel tankering system mayimplement a dynamic fuel tankering process that involves calculating arespective fuel uplift quantity for each arrival station in anaircraft's flight route. More specifically, the fuel uplift or tankeringquantities may be calculated to decrease or minimize the total operatingcosts of the aircraft including the total fuel consumption costs anddelay costs.

The process may involve generating a model that adheres to the operatingrestrictions of the aircraft and that can be used to perform thecalculation of the respective fuel uplift quantity at each arrivalstation. Additionally, the model may account for the fuel prices,weather conditions, and other changing factors in each sector whenperforming the calculation. The fuel tankering system may periodicallyperform the process during the aircraft's flights in order to update thefuel uplift calculation to account for any changes that may affect thetotal cost of operating the aircraft. Additionally, the recalculationmay be performed to update values of variables that define the model.For example, after each landing, the fuel uplift amount is recalculatedconsidering updated flight data (e.g., actual remaining fuel afterlanding). Such a dynamic fuel tankering procedure may result inincreased cost-savings in comparison to the tankering proceduredescribed above.

Implementations of this disclosure provide technological improvementsthat are particular to computer networks and computing systems, forexample, computing systems of an airline flight dispatch system.

Computing system-specific technological problems, such as the managementand use of large quantities of complex data stemming from multiplesources, as well as inefficiency associated therewith, can be wholly orpartially solved by the implementations of this disclosure. For example,implementation of this disclosure may generate complex fuel tankeringmodels for aircraft operated by airlines. In some examples, each modelmay be defined by 5*(N−1) equations, where N is the number of stationsin an aircraft's flight route. And each of these equations may be afunction of one or more portions of the complex data. Accordingly,implementations of this disclosure make it feasible to generate and usesuch complex models. As another example, implementation of thisdisclosure increases the accuracy and reliability of calculations todetermine fuel uplift quantities at stations, which in turn decreasesthe overall cost of operating an aircraft. As yet another example,implementation of this disclosure may utilize real-time data in order torapidly update the generated models, which in turn can result in lowerfuel consumption costs and delay costs.

Implementations of this disclosure can thus introduce new and efficientimprovements in the ways in which fuel uplift quantities are calculated,and in turn facilitate new and efficient improvements in the ways inwhich aircraft may be operated. Implementations of this disclosure cancondense and perform calculations with large amounts of information inorder to generate information interpretable for the purpose of quicklydetermining the fuel uplift quantities at each station for an aircraft.

II. Fuel Tankering

FIG. 1 depicts a flight route 100 for an aircraft 102, according to anexample embodiment. The aircraft 102 may be one of many aircraftoperated by an airline, and the flight route 100 may be determined by aflight planning system of the airline. The flight route 100 may be adaily flight route for the aircraft 102 that includes each flight thatis scheduled to depart within a calendar day. In some scenarios, a finalflight in the flight route 100 may be scheduled to depart on onecalendar day and land on the following calendar day.

As shown in FIG. 1, the flight route 100 may include a plurality offlight sectors 104, 106, and 108. Each flight sector may include aflight between a departure station and an arrival station. For example,flight sector 104 (also labelled as “Sector 1”) may include a flightbetween station 110 (also labelled as “Location 1”) and station 112(also labelled as “Location 2”). And flight sector 106 may include aflight between station 112 and station 114. Within examples, a flightroute may include N−1 flight sectors, where N is a number of stations inthe flight route. For instance, a flight route may include four stationsand three flight sectors.

The aircraft 102 may be scheduled to sequentially fly flight sectors104, 106, 108. For example, the aircraft 102 may be scheduled to flyflight sector 104, followed by flight sector 106, and so on, until theaircraft 102 lands at a final station 116 (also labelled as “LocationN”).

In line with the discussion above, the airline may seek to decrease theoperating costs of the aircraft 102. In an embodiment, the airline maydo so by implementing a dynamic fuel tankering procedure for theaircraft 102. As explained herein, by implementing a dynamic fueltankering procedure for the aircraft 102, the airline may determine, inreal-time (i.e., during the aircraft's journey along the flight route),a respective quantity of fuel to be supplied to the aircraft 102 at eachstation so as to decrease or minimize the fuel consumption costs and/orthe delay costs of the aircraft.

FIG. 2A illustrates a fuel tankering system 200, according to an exampleembodiment. In an embodiment, the fuel tankering system 200 may beimplemented within a flight dispatch system (not illustrated) of theairline. Furthermore, the fuel tankering system 200 may be embodied byone or more computing devices (e.g., computing system 500 of FIG. 5). Asshown in FIG. 2A, the fuel tankering system 200 may include a centralcomputing system 202, a disruption monitor computing system 208, aflight planning computing system 212, and a flight operations database214. Components of the fuel tankering system 200 may be linked togetherby a system bus, network, or other connection mechanism 216.Furthermore, although each of the components is depicted as a separatecomponent, they may be embodied by the same one or more computingdevices.

In an embodiment, the central computing system 202 may be configured togenerate a linear model 204 for calculating the respective fuel upliftquantities 206 to be supplied to an aircraft at each station in theaircraft's flight route. More specifically, the linear model 204 maycalculate the respective fuel uplift quantities 206 so as to decreasethe total operating costs of the aircraft. In order to calculate fueluplift quantities that are feasible, the linear model 204 may adhere tooperating restrictions on the aircraft. The operating restrictions maybe regulatory restrictions and/or design restrictions. Additionally, inorder to increase the accuracy of the calculation, the linear model 204may rely on real-time data and predicted data that is based onhistorical values.

In an embodiment, the operating restrictions may include: (i) a maximumstructural takeoff weight (MTOW) at departure stations may not beexceeded, (ii) a maximum landing weight (MLW) at arrival stations maynot be exceeded, (iii) a maximum fuel capacity (MAXF) for the aircraftmay not be exceeded, (iv) a quantity of fuel supplied at a station maynot be less than a minimum quantity required by regulations, and (iv) aremaining quantity of fuel at arrival stations may not be less than aminimum reserve quantity (e.g., as defined by an airline's operatingpolicies).

In order to satisfy the operating restrictions, the linear model 204 maybe a constrained optimization model that may be configured to minimizethe total operating cost of the aircraft while adhering to the operatingrestrictions. In this approach, an objective function of the model maybe optimized with respect to variables that have constraints. Here, theobjective function may be to decrease or minimize the total cost ofoperating the aircraft. And the constraints on variables of the modelmay be the operating restrictions on the aircraft.

In an implementation, the linear model 204 may be a linear programmingmodel, which is a constrained optimization model in which therelationships between the variables that define the model are linear.The linear programming model may minimize the operating costs of anaircraft that is scheduled to fly to N stations in i sectors. Thedecision variable of the linear programming model may be the quantity offuel (X_(i)) to be supplied to the aircraft in each sector i, where i=1,. . . , N−1.

FIG. 2B depicts equations that define the linear model 204, according toan example embodiment. Equation 218 may represent the objective functionof the linear programming model as:

MinZ=Σ _(i=0) ^(N−1) P _(i) *X _(i)  (218)

More specifically, the equation 218 indicates that the objectivefunction is to minimize the fuel consumption cost. The fuel consumptioncost is the sum of the cost of the quantity of fuel (X_(i)) refueled ateach arrival station in each sector i. At an arrival station in sectori, the cost of the fuel is the price of the fuel at station (P_(i)) in($/kg) multiplied by the quantity of fuel supplied at the station(X_(i)) in (kg).

FIG. 2B further depicts equations 220, 222, 224, 226, and 228 thatrepresent the operating restrictions on the linear programming model.Inequality 220 defines the operating restriction that the MTOW atdeparture stations may not be exceeded as:

ZFW _(i) +FOB _(i) ≤MTOW _(i) for i=1, . . . , N−1  (220)

In particular, inequality 220 states that the sum of the zero fuelweight (in kg) of the aircraft when in sector i (ZFW_(i)) and the totalweight of the fuel (in kg) aboard the aircraft in sector i (FOB_(i)) isless than or equal to the MTOW (in kg) in sector i (MTOW_(i)). The ZFWof an aircraft is the total weight (in kg) of the aircraft and all ofits contents minus the total weight of the usable fuel on board.

Inequality 222 represents the restriction that the MLW at arrivalstations may not be exceeded as:

TOW _(i) TRIP _(i) ≤MLW _(i) for i=1, . . . , N−1  (222)

In particular, inequality 222 states that the difference between thetakeoff weight (in kg) of the aircraft in sector i (TOW_(i)) and thefuel consumption (in kg) in sector i (TRIP_(i)) is less than or equal tothe MLW in sector i (MLW).

Inequality 224 represents the restriction that the maximum aircraft fuelcapacity (MAXF) may not be exceeded as:

REM _(i) +X _(i) ≤MAXF for i=1, . . . , N−1  (224)

In particular, inequality 224 states that the sum of the quantity ofremaining fuel after landing in sector i (REM_(i)) and the quantity offuel to be refueled in sector i (X_(i)) is less than or equal to theMAXF for the aircraft.

Inequality 226 represents the restriction that the amount of fuelsupplied at a station may not be less than the regulatory requiredminimum as:

REM _(i) +X _(i) ≥FOB0_(i) for i=1, . . . , N−1  (226)

In particular, inequality 226 states that the sum of the quantity ofremaining fuel after landing in sector i (REM_(i)) and the quantity offuel to be refueled in sector i (X_(i)) is greater than or equal to theregulatory required minimum quantity of fuel in sector i (FOB0_(i)).

Inequality 228 represents the restriction that the remaining amount offuel at an arrival station may not be less than a minimum reservequantity of fuel as:

FOB _(i−1) +TRIP _(i−1) ≥MINF _(i) for i=2, . . . , N−1  (228)

In particular, inequality 228 states that the sum of the total quantityof fuel aboard the aircraft in the previous sector (FOB_(i−1)) and thequantity of the fuel consumed in the previous sector (TRIP_(i−1)) isgreater than or equal to the quantity of fuel remaining in sector i(MINF_(i)) before the aircraft is refueled.

FIG. 2B also depicts equations 230, 232, 234, 236, and 238 which definesome of the variables in the equations that represent the restrictions.Equation 230 defines the quantity of the fuel consumed in the sector i(TRIP_(i)) as:

TRIP _(i) =f _(i). (FOB _(i) −FOB0_(i))+TRIP0_(i)+ϕ_(a) . TGD _(i) +TAD_(i). ϕ_(g) for i=1, . . . , N−1  (230)

As shown by equation 230, TRIP_(i) may depend on a consumptionadjustment factor f in sector i (f_(i)), the quantity of fuelconsumption from sector i without tankering (TRIP0_(i)), the totalweight of the fuel aboard on sector i (FOB_(i)), the regulation minimumfuel on sector i (FOB0_(i)), and delay variables TGD_(i), TAD_(i),ϕ_(a), ϕ_(g). TGD_(i) and TAD_(i) may represent the expected times ofground delay and air delay, respectively, in sector i, and ϕ_(a) andϕ_(g) represent fuel consumption rates (e.g., kg/sec) on the ground andin the air, respectively.

Equation 232 defines consumption adjustment factor f_(i) as:

$\begin{matrix}{f_{i} = \frac{{dW}_{f}}{dW}} & (232)\end{matrix}$

As shown by equation 232, f_(i) may be defined as the variation of thefuel consumed (dW_(f)) over the variation in aircraft weight (dW). Theresulting f_(i) is a function of altitude and travel distance of thataircraft, and accounts for variations in fuel consumption due tovariations in the altitude and travel distance. Additionally, f_(i) maybe sensitive to cruising altitude and speed of the aircraft in sector i,and the average wind speed in the sector.

Equation 234 defines the quantity of fuel aboard the aircraft in sectori (FOB_(i)) as:

FOB _(i) =REM _(i) +X _(i) for i=1, . . . , N−1  (234)

In particular, FOB in sector i (FOB_(i)) is equal to the sum of theremaining quantity of fuel after the aircraft lands in sector i(REM_(i)) and the quantity of fuel to be refueled in sector i (X_(i)).

Equation 236 defines the quantity of remaining fuel at destinationstations as:

REM _(i) =FOB _(i−1) −TRIP _(i−1) for i=1, . . . , N−1  (236)

In particular, the remaining fuel after landing in sector i (REM_(i)) isequal to the difference between the total quantity of fuel aboard in theprevious sector (FOB_(i−1)) and the quantity of fuel consumed in theprevious sector (TRIP_(i−1)).

Finally, FIG. 2B depicts equation 238 that defines the restriction thatonly positive quantities of fuel may be supplied in each sector i:

X_(i)≥0  (238)

Note that the equations regarding one sector i are linear combinationsof the equations regarding the sector before (i−1), lending the linearmodel 204 a recursive nature. Also, the number of restrictions may be afunction of the number of stations. In particular, 5*(N−1) restrictionsare generated for a flight route, where N is the number of stations. Forexample, the linear model 204 for an aircraft that has between four totwelve sectors in its flight route may have twenty to sixty restrictionsand associated equations.

Returning to FIG. 2A, the disruption monitor computing system 208, theflight planning computing system 212, and the flight operations database214 may provide the central computing system 202 with data forgenerating the linear model 204. The data may be used to determinevalues of the variables of the equations that define the linear model204. For example, the disruption monitor computing system 208 maygenerate a list of available stations 210. As described herein, the listof stations may provide the linear model 204 with real-time dataindicative of the available stations along the aircraft's flight route.

In order to provide the central computing system 202 with real-timedata, the fuel tankering system 200, or one or more components thereof,may access one or more remote server(s) 240 (e.g., Internet servers) toreceive real-time or approximately real-time data. For instance, inorder to generate or update the available stations 210, the disruptionmonitor computing system 208 may poll the server 240, continuously orperiodically, for real-time weather information that may be used togenerate or update the available stations 210. As described herein,other data may additionally or alternatively be requested from theserver 240.

FIG. 3 illustrates a flowchart 300 of a dynamic fuel tankering process,according to an example embodiment. The process illustrated by FIG. 3may be carried out by the fuel tankering system 200. However, theprocess can be carried out by other types of devices or devicesubsystems operated by an airline.

In an embodiment, the fuel tankering system 200 may implement the fueltankering process for an aircraft in order to calculate a fuel upliftquantity at each station in the aircraft's flight route. The fueltankering system 200 may perform the process periodically as theaircraft travels along the flight route. That is, the fuel tankeringsystem 200 may periodically perform the process to periodically updatethe fuel uplift quantities for remaining flight stations in the flightroute. Doing so allows the fuel tankering system 200 to update thecalculation to account for factors that may be changing constantly(e.g., weather, airport availability, delay times, fuel prices, etc.).The fuel tankering system may also update the calculation to use actualdata (as opposed to predicted data) as the fuel tankering system 200acquires the data.

In an embodiment, one or more components of the fuel tankering system200 may be involved in the performance of the fuel tankering process.More specifically, as indicated by the dotted lines in FIG. 3, theflight operations database 214 and the flight planning computing system212 may provide data that may be used in different steps of the fueltankering process.

As shown in FIG. 3, the flight operations database 214 may store dataincluding NOTAM data 330 (e.g., indicative of airport closures orhazards in the flight route), weather data 332 (e.g., indicative of theweather in each sector of the flight route), station status 334 (e.g.,indicative of the availability of gates, fuel supply, and status ofmaintenance and baggage handling, etc. at the stations in the flightroute), estimated time of arrival/estimated time of departure (ETA/ETD)data 336, remaining fuel data 338 (e.g., a regulatory required minimumquantity of fuel on the aircraft), fuel prices data 340, payload data358, aircraft data 360, logistical constraints data 342 (includes MTOW344, MLW 346, and MAXF 348), and delay data 350 (includes in-flightdelays 352 and ground delays 354).

Within examples, at least some of the data stored in the flightoperations database 214 is data that is obtained in real-time from aplurality of sources. As explained above, using real-time data in eachiteration of the process facilitates an accurate and up-to-datecalculation of the respective fuel uplift quantities. The flightoperations database 214 may poll the plurality of sources, continuouslyor periodically, in order to receive the real-time data. In an example,the NOTAM data 330 may be requested from and received in real-time froma regulatory authority. In another example, the weather data 332 may bereal-time weather data that is requested and received from one or moreweather forecasting services. In yet another example, the station status334 may be real-time data that is requested and received from scheduledstations in the flight route. The real-time data may include a status ofbaggage handling services, a status of baggage maintenance services, andgate availability at the station. In yet another example, the payloaddata 358 may real-time data received from stations.

Other data stored in the flight operations database 214 may be fromairline manuals and operating procedures, aircraft manufacturer manuals,regulatory publications, etc. Yet other data may be generated by theflight operations database 214 based on historical flight data stored inthe database.

As shown in FIG. 3, the flight planning computing system 212 may storetrip fuel data 322 and remaining fuel data 324. The trip fuel data 322may be indicative of the quantity of fuel dedicated to a particularflight in the flight route. And the remaining fuel data 324 may beindicative of the remaining fuel on board the aircraft at a given time.

In an embodiment, a first iteration of the fuel tankering process may beperformed prior to the aircraft departing a first station in theaircraft's daily flight route. The process may then be periodicallyperformed until the aircraft lands at the final station in the dailyflight route. For example, the method may be periodically performedevery 15 minutes, whether the aircraft is in-flight or on-ground. Othertime periods on the order of minutes or hours may be possible andcontemplated herein.

As shown by FIG. 3, a first step 302 of the process may be the fueltankering system 200 initializing, perhaps in response to a trigger. Forexample, the trigger may be an indication that the aircraft is scheduledto fly from a first station, perhaps within a predefined period of time.For instance, the fuel tankering system 200 may be initialized inresponse to an indication that the aircraft is scheduled to begin departthe first station in one hour.

Once the system is initialized, the fuel tankering system 200 maytransition to step 304 of generating a list of available stations alongthe aircraft's flight route. As explained above, the list of availablestations may be generated by the disruption monitor computing system208. In order to generate the list, the disruption monitor computingsystem 208 may receive from the flight operations database 214 dataincluding: (i) a scheduled flight route for the aircraft, (ii) noticesto airmen (NOTAMs), (iii) real-time weather data along the flight route,(iv) real-time statuses of the scheduled arrival stations, and (v)logistical constraints.

The disruption monitor computing system 208 may use the data todetermine if any of the scheduled stations in the aircraft's flightroute are unavailable. For example, the disruption monitor computingsystem 208 may verify the availability of the scheduled stations alongthe aircraft flight route based on weather conditions (e.g., stationclosures due to low ceiling/visibility), station status (e.g., lack offuel supply and handling or maintenance services at a station), andNOTAMs. If the disruption monitor computing system 208 determines that ascheduled station is unavailable, the disruption monitor computingsystem 208 may select a different station to replace the unavailablestation. In an implementation, the disruption monitor computing system208 may be configured to select an available station that is within athreshold distance from the unavailable station. The disruption monitorcomputing system 208 may then update the flight route of the aircraft toinclude the replacement station. In scenarios where the disruptionmonitor computing system 208 does not identify a replacement station,perhaps due to widespread weather issues in a geographical region, thedisruption monitor computing system 208 may alter the flight route byremoving the station. The disruption monitor computing system 208 maythen provide a list of the available stations to the central computingsystem 202.

Once the list of available stations is generated, the fuel tankeringsystem 200 may transition to step 306 of generating a linear model 204to calculate the respective fuel uplift quantities at the remainingflight stations. This step may be performed by the central computingsystem 202. In addition to receiving the available stations from thedisruption monitor computing system 208, the central computing system202 may receive from the flight operations database 214 data that may beused to generate the linear model. The data received may include dataindicative of: (i) real-time fuel prices at each of the availablestations, (ii) real-time delays, estimated time of arrival (ETA) andestimated time of departure (ETD) of the aircraft at each station, (iii)and an amount of remaining fuel in the aircraft.

In a first instance or iteration of the process (i.e., before theaircraft departs the first station), the delays (in-flight and ground)may be estimated based on historical delay data stored in the flightoperations database 214. In subsequent iterations, the delay data mayinclude real-time delay data from a live flight track system for theairline. Additionally and/or alternatively, the delay data may begenerated by a flight management computer (FMC) on the aircraft. In anexample, the delay data may be the FMC's ETA/ETD predictions outside ±15min interval from the scheduled times. Accordingly, the delay dataaccommodates in-flight delays related to air traffic flow measuresadopted by air-traffic controllers (ATCs) such as holdings, speedreductions or ground delay programs.

In an embodiment, the central computing system 202 may use the receiveddata to determine values for the variables in the equations (e.g.,equations 218-228 in FIG. 2B) that define the linear model 204.

For example, equation 220 includes variables ZFW_(i) and MTOW_(i).ZFW_(i) may be determined based on aircraft data 360 and payload data358. In particular, aircraft data 360 may be indicative of a grossweight of the aircraft, and payload data 358 may be indicative of thecargo and passengers (e.g., number of passengers) for the flight insector i. From this data, the weight of the aircraft and the payload maybe determined, which may then be summed to determine ZFW_(i). MTOW_(i)may be calculated using software produced by the aircraft manufacturerfor calculating takeoff and landing performance. For this calculation,real-time weather data 332 such as temperatures and calm wind may beused. The central computing system 202 may similarly use the receiveddata to define each of the remaining equations 220-228.

Once the linear model is generated, the fuel tankering system 200 maytransition to state 308 of using the linear model to calculaterespective fuel uplift quantities for each of the remaining flightstations in the aircraft's flight route. In particular, the calculationmay be performed by the central computing system 202. In order toperform the calculation, the central computing system 202 may receivefrom the flight planning computing system 212 data indicative of thetrip fuel data 322 and the remaining fuel data 324 in the aircraft. Theflight planning computing system 212 may calculate the trip fuel basedon payload data 358, weather data 332, and aircraft data 360 receivedfrom the flight operations database 214.

Once the central computing system 202 receives the data indicative ofthe trip fuel data 322 and the remaining fuel data 324, the centralcomputing system may use the linear model to determine the respectivefuel uplift quantities in each of the remaining flight stations. Inparticular, the trip fuel data 322 and the remaining fuel data 324 maybe used, in addition to the data provided to the central computingsystem 202 in step 306, to fully characterize the linear model. Thecharacterized linear model may then be used to calculate the respectivefuel uplift quantities. As explained above, the objective function ofthe linear model may be to minimize operating costs by minimizing fuelconsumption costs and delay costs. Accordingly, the linear model may beused to calculate respective fuel uplift quantities that minimize theoperating costs of the aircraft.

Once the calculation is complete, the fuel tankering system 200 maytransition to decision state 310. In this state, the fuel tankeringsystem 200 may determine if the aircraft has completed the last flightsector in the daily flight route. The calculation cycle ends after thelanding of the last flight. In this case the model may be run for thelast time, using historical data, to predict the first fuel uplift inthe first sector of the next day. If the last flight sector has beencompleted, then the fuel tankering process may be ended for the aircraftfor that day, and the fuel tankering system 200 may transition to an endstate 312.

On the other hand, if the aircraft has not yet completed the last flightsector, then the fuel tankering system 200 may transition to state 314in which the fuel tankering system 200 may display the respective fueluplift quantities on an uplift tracking display device. The displaydevice may be a display device of the fuel tankering system 200 or adisplay device of the flight dispatch system. After each iteration ofthe process, the fuel tankering system 200 may update the respectivefuel uplift quantities for the remaining flight sectors that aredisplayed on the display device.

FIG. 4 illustrates a graphical user interface (GUI) 400 of the uplifttracking display device, according to an example embodiment. As shown inFIG. 4, the GUI 400 may include an aircraft identifier 402, such as theaircraft tail number. Additionally, the GUI 400 may display arepresentation 404 of the flight route of the aircraft. Therepresentation may include an identifier of one or more of the flightstations in the flight route. For example, the identifier may be anairport code of the station. Further, the GUI 400 may include a flightevolution bar 406 that indicates a current location of the aircraft.Stations located to the left of the flight evolution bar 406 arestations 408 that the aircraft has already departed from, and stationslocated to the right are remaining stations 410 in the flight route.Additionally, the GUI 400 may display the actual and estimated departuretimes from the stations, such as actual departure time 412.

The GUI 400 may also display the respective fuel tankering quantitiesabove the respective the respective identifier of the stations. Inparticular, for stations 408, the fuel tankering quantity displayed isthe actual fuel uplift quantity refueled at the stations. And forstations 410, the fuel uplift quantity displayed is the calculated fueluplift quantity to be refueled at the stations. As explained above, theGUI 400 may be updated during each iteration of the fuel tankeringprocess disclosed herein. Accordingly, the fuel uplift tracking displaymay provide easy access to an up-to-date fuel uplift quantities for anaircraft.

III. Computing Device

FIG. 5 illustrates an example computing system 500, according to anexample embodiment. In some examples, components illustrated in FIG. 5may be distributed across multiple computing devices or computingsystems. However, for the sake of example, the components are shown anddescribed as part of one example computing system 500. The computingsystem 500 may be or include a mobile device (such as a mobile phone), adesktop computer, a laptop computer, a tablet computer, a server, anetwork of multiple servers, or similar device(s) that may be configuredto perform the functions described herein.

As shown in FIG. 5, the computing system 500 includes one or moreprocessors 502, one or more non-transitory computer readable media 504,a communication interface 506, a display 508, and a user interface 510.Components illustrated in FIG. 5 may be linked together by a system bus,network, or other connection mechanism 512.

The one or more processors 502 can be any type of processor(s), such asa microprocessor, a digital signal processor, a multicore processor,etc., coupled to the one or more non-transitory computer readable media504. The one or more non-transitory computer readable media 504 can beany type of memory, such as volatile memory like random access memory(RAM), dynamic random access memory (DRAM), static random access memory(SRAM), or non-volatile memory like read-only memory (ROM), flashmemory, magnetic or optical disks, or compact-disc read-only memory(CD-ROM), among other devices used to store data or programs on atemporary or permanent basis.

Additionally, the one or more non-transitory computer readable media 504can be configured to store instructions 514. The instructions 514 may beexecutable by the one or more processors 502 to cause the computingsystem 500 to perform any of the functions described herein.

The communication interface 506 can include hardware to enablecommunication within the computing system 500 and/or between thecomputing system 500 and one or more other devices. The hardware caninclude transmitters, receivers, and antennas, for example. Thecommunication interface 506 can be configured to facilitatecommunication with one or more other devices, in accordance with one ormore wired or wireless communication protocols. For example, thecommunication interface 506 can be configured to facilitate wirelessdata communication for the computing system 500 according to one or morewireless communication standards, such as one or more IEEE 801.11standards, ZigBee standards, Bluetooth standards, etc. As anotherexample, the communication interface 506 can be configured to facilitatewired data communication with one or more other devices.

The display 508 can be any type of display component configured todisplay data. As one example, the display 508 can include a touchscreendisplay. As another example, the display 508 can include a flat-paneldisplay, such as a liquid-crystal display (LCD) or a light-emittingdiode (LED) display.

The user interface 510 can include one or more pieces of hardware usedto provide data and control signals to the computing system 500. Forinstance, the user interface 510 can include a mouse or a pointingdevice, a keyboard or a keypad, a microphone, a touchpad, or atouchscreen, among other possible types of user input devices.Generally, the user interface 510 can enable an operator to interactwith a graphical user interface (GUI) provided by the computing system500 (e.g., displayed by the display 508).

IV. Example Operations

FIG. 6A is a flow chart illustrating a method 600, according to anexample embodiment. The method 600 illustrated by FIG. 6A may be carriedout by a computing device, such as computing system 500.

The embodiments of FIG. 6A may be simplified by the removal of any oneor more of the features shown therein. Further, these embodiments may becombined with features, aspects, and/or implementations of any of theprevious figures or otherwise described herein.

Within examples, the process of FIG. 6A may be a process for dynamically(e.g., as values of changing factors are polled and received from aplurality of sources) calculating a total fuel uplift quantity for anaircraft to account for changing factors as the aircraft flies along aflight route that comprises a plurality of flight sectors, where aflight sector includes a respective flight between a respectivedeparture station and a respective arrival station.

Block 602 may involve: (a) polling, by a computing system, in real-timea plurality of sources to receive data indicative of: (i) real-timerespective weather conditions in each of one or more remaining flightsectors in a flight route, and (ii) respective delay information for therespective flights in the remaining flight sectors. In an embodiment,the computing system may poll the plurality of sources in response to anindication that a particular variable (e.g., weather conditions, delays,airport status, or any of the changing variables described herein). Inanother embodiment, the computing system may periodically poll theplurality of sources to receive updated data, if any.

Block 604 may involve (b) calculating for the remaining flight sectors arespective fuel consumption factor, wherein the respective fuelconsumption factor is a function of altitude and travel distance thataccounts for variations in fuel consumption due to variations inaltitude and travel distance of the aircraft.

Block 606 may involve (c) based on (i) respective fuel quotations at theremaining arrival stations, (ii) the real-time weather conditions, and(iii) the respective delay information for the respective flights in theremaining flight sectors, generating a linear model for calculating arespective fuel uplift quantity at each remaining arrival station,wherein the respective fuel uplift quantity includes a respectivequantity of fuel with which the aircraft is refueled at each remainingarrival station.

Block 608 may involve (d) calculating using the linear model therespective fuel uplift quantity at each remaining arrival station,wherein the total fuel uplift quantity is a sum of the respective fueluplift quantities at the arrival stations in the flight route.

Block 610 may involve (e) periodically performing operations (a)-(d) toupdate a calculation of the respective fuel uplift quantities.

In some embodiments, a first instance of performing operations (a)-(d)occurs prior to the aircraft departing the respective departure stationof a first flight sector.

FIG. 6B depicts a block 614 of a method 612 that is related to themethod 600. At block 614, the method 612 includes polling in real-timethe plurality of sources to receive data indicative of: real-timepayload information for the respective flights in the remaining flightsectors. In particular, in some embodiments, block 602 of polling inreal-time the plurality of sources may further include polling inreal-time the plurality of sources to receive data indicative of:real-time payload information for the respective flights in theremaining flight sectors.

FIG. 6C depicts a block 618 of a method 616 that is related to themethod 612. At block 618, the method 616 includes based on the real-timerespective weather conditions and the real-time payload information,calculating a quantity of fuel needed for the respective flights,wherein generating the linear model is further based on the quantity offuel needed for the respective flights. In particular, in someembodiments, block 618 may be an additional block of the method 612.

FIG. 6D depicts a block 622 of a method 620 that is related to themethod 616. At block 622, the method 620 includes calculating anaircraft delay quantity of fuel for consumption as a result of aircraftdelay. In particular, in some embodiments, block 618 of calculating aquantity of fuel needed for the respective flights may further includecalculating a quantity of fuel for consumption as a result of aircraftdelay.

FIG. 6E depicts a block 626 of a method 624 that is related to themethod 600. At block 626, the method 624 includes polling in real-timethe plurality of sources to receive a remaining quantity of fuel on theaircraft, and wherein generating the linear model is further based onthe remaining quantity of fuel on the aircraft. In particular, in someembodiments, block 602 of polling in real-time the plurality of sourcesmay further include polling in real-time the plurality of sources toreceive a remaining quantity of fuel on the aircraft.

FIG. 6F depicts blocks 630 and 632 of a method 628 that is related tothe method 600. At block 630, the method 628 includes polling inreal-time the plurality of sources to receive data indicative ofreal-time logistical constraints at the remaining arrival stations. Atblock 632, the method 628 includes based on (i) the real-time respectiveweather conditions, (ii) the real-time logistical constraints at theremaining arrival stations and (iii) notice to airmen (NOTAM)constraints, determining a list of available of remaining arrivalstations in the flight route, wherein the linear model is further basedon the list of available of remaining arrival stations in the flightroute In particular, in some embodiments, block 602 of polling inreal-time the plurality of sources may further include polling inreal-time the plurality of sources to receive data indicative ofreal-time logistical constraints at the remaining arrival stations.Furthermore, block 632 may be an additional block of the method 600.

FIG. 6G depicts a block 636 of a method 634 that is related to themethod 600. At block 636, the method 634 includes providing, to adisplay device, a representation of an interface that includes: (i) aflight track timeline that indicates the arrival stations in the flightroute, wherein for arrival stations of previous flight sectors thetimeline further indicates a respective actual departure time and arespective actual uplift quantity, and wherein for the remaining arrivalstations the timeline further indicates a respective estimated time andthe respective uplift quantity, and (ii) an identifier of the aircraft.In particular, in some embodiments, block 636 may be an additional blockof method 600.

In some embodiments, generating the linear model is further based on aplurality of logistical constraints including: a maximum takeoff weight(MTOW) for the aircraft, a maximum landing weight (MLW) for theaircraft, a maximum fuel capacity (MAXF) for the aircraft, a minimumfuel uplift quantity required for the respective flights, and arespective minimum quantity of reserve fuel for each arrival station.

In some embodiments, the real-time logistical constraints at theremaining arrival stations include at least one of: respective fuelsupplies, respective statuses of handling services, respective statusesof maintenance services, respective gate availabilities, and respectiveoperational statuses.

In some embodiments, the linear model is linear programming model, wherean objective function of the linear programming model is to minimize thetotal fuel uplift quantity, and where a decision variable of the linearprogramming model is the respective fuel uplift quantities.

In some embodiments, the objective function of the linear programmingmodel further is to minimize delay costs along the flight route.

In some embodiments, the respective delay information for the respectiveflights comprises an estimated delay and an actual delay, where theestimated delay is based on historical flight data related to the flightroute.

V. Conclusion

The description of the different advantageous arrangements has beenpresented for purposes of illustration and description, and is notintended to be exhaustive or limited to the examples in the formdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art. Further, different advantageous examplesmay describe different advantages as compared to other advantageousexamples. The example or examples selected are chosen and described inorder to explain the principles of the examples, the practicalapplication, and to enable others of ordinary skill in the art tounderstand the disclosure for various examples with variousmodifications as are suited to the particular use contemplated.

We claim:
 1. A method for dynamically calculating a total fuel upliftquantity for an aircraft to account for changing factors as the aircraftflies along a flight route that comprises a plurality of flight sectors,wherein a flight sector comprises a respective flight between arespective departure station and a respective arrival station, themethod comprising: (a) polling in real-time a plurality of sources toreceive data indicative of: (i) real-time respective weather conditionsin each of one or more remaining flight sectors in the flight route, and(ii) respective delay information for the respective flights in theremaining flight sectors; (b) calculating for the remaining flightsectors a respective fuel consumption factor, wherein the respectivefuel consumption factor is a function of altitude and travel distancethat accounts for variations in fuel consumption due to variations inaltitude and travel distance of the aircraft; (c) based on (i)respective fuel quotations at the remaining arrival stations, (ii) thereal-time weather conditions, and (iii) the respective delay informationfor the respective flights in the remaining flight sectors, generating alinear model for calculating a respective fuel uplift quantity at eachremaining arrival station, wherein the respective fuel uplift quantityincludes a respective quantity of fuel with which the aircraft isrefueled at each remaining arrival station; (d) calculating using thelinear model the respective fuel uplift quantity at each remainingarrival station, wherein the total fuel uplift quantity is a sum of therespective fuel uplift quantities at the arrival stations in the flightroute; and (e) periodically performing operations (a)-(d) to update acalculation of the respective fuel uplift quantities.
 2. The method ofclaim 1, wherein a first instance of performing operations (a)-(d)occurs prior to the aircraft departing the respective departure stationof a first flight sector.
 3. The method of claim 1, wherein polling inreal-time the plurality of sources further comprises: polling inreal-time the plurality of sources to receive data indicative of:real-time payload information for the respective flights in theremaining flight sectors.
 4. The method of claim 3, wherein the methodfurther comprises: based on the real-time respective weather conditionsand the real-time payload information, calculating a quantity of fuelneeded for the respective flights, wherein generating the linear modelis further based on the quantity of fuel needed for the respectiveflights.
 5. The method of claim 4, wherein calculating a quantity offuel needed for the respective flights comprises: calculating anaircraft delay quantity of fuel for consumption as a result of aircraftdelay.
 6. The method of claim 1, wherein polling in real-time theplurality of sources further comprises: polling in real-time theplurality of sources to receive a remaining quantity of fuel on theaircraft, and wherein generating the linear model is further based onthe remaining quantity of fuel on the aircraft.
 7. The method of claim1, wherein generating the linear model is further based on a pluralityof logistical constraints comprising: a maximum takeoff weight (MTOW)for the aircraft, a maximum landing weight (MLW) for the aircraft, amaximum fuel capacity (MAXF) for the aircraft, a minimum fuel upliftquantity required for the respective flights, and a respective minimumquantity of reserve fuel for each arrival station.
 8. The method ofclaim 1, wherein polling in real-time the plurality of sources furthercomprises polling in real-time the plurality of sources to receive dataindicative of real-time logistical constraints at the remaining arrivalstations, and wherein the method further comprises: based on (i) thereal-time respective weather conditions, (ii) the real-time logisticalconstraints at the remaining arrival stations and (iii) notice to airmen(NOTAM) constraints, determining a list of available of remainingarrival stations in the flight route, wherein the linear model isfurther based on the list of available of remaining arrival stations inthe flight route.
 9. The method of claim 8, wherein the real-timelogistical constraints at the remaining arrival stations comprise atleast one of: respective fuel supplies, respective statuses of handlingservices, respective statuses of maintenance services, respective gateavailabilities, and respective operational statuses.
 10. The method ofclaim 1, wherein the linear model is linear programming model, whereinan objective function of the linear programming model is to minimize thetotal fuel uplift quantity, and wherein a decision variable of thelinear programming model is the respective fuel uplift quantities. 11.The method of claim 1, further comprising: providing, to a displaydevice, a representation of an interface that includes: a flight tracktimeline that indicates the arrival stations in the flight route,wherein for arrival stations of previous flight sectors the timelinefurther indicates a respective actual departure time and a respectiveactual uplift quantity, and wherein for the remaining arrival stationsthe timeline further indicates a respective estimated time and therespective uplift quantity, and an identifier of the aircraft.
 12. Themethod of claim 1, wherein the respective delay information for therespective flights comprises an estimated delay and an actual delay,wherein the estimated delay is based on historical flight data relatedto the flight route.
 13. A computing system for dynamically calculatinga total fuel uplift quantity for an aircraft to account for changingfactors as the aircraft flies along a flight route that comprises aplurality of flight sectors, wherein a flight sector comprises arespective flight between a respective departure station and arespective arrival station, the computing system comprising: a memorythat stores instructions; and a processor configured to execute theinstructions to perform operations comprising: (a) polling in real-timea plurality of sources to receive data indicative of: (i) real-timerespective weather conditions in each of one or more remaining flightsectors in the flight route, and (ii) respective delay information forthe respective flights in the remaining flight sectors; (b) calculatingfor the remaining flight sectors a respective fuel consumption factor,wherein the respective fuel consumption factor is a function of altitudeand travel distance that accounts for variations in fuel consumption dueto variations in altitude and travel distance of the aircraft; (c) basedon (i) respective fuel quotations at the remaining arrival stations,(ii) the real-time weather conditions, and (iii) the respective delayinformation for the respective flights in the remaining flight sectors,generating a linear model for calculating a respective fuel upliftquantity at each remaining arrival station, wherein the respective fueluplift quantity includes a respective quantity of fuel with which theaircraft is refueled at each remaining arrival station; (d) calculatingusing the linear model the respective fuel uplift quantity at eachremaining arrival station, wherein the total fuel uplift quantity is asum of the respective fuel uplift quantities at the arrival stations inthe flight route; and (e) periodically performing operations (a)-(d) toupdate a calculation of the respective fuel uplift quantities.
 14. Thecomputing system of claim 13, wherein a first instance of performingoperations (a)-(d) occurs prior to the aircraft departing the respectivedeparture station of a first flight sector.
 15. The computing system ofclaim 13, wherein polling in real-time the plurality of sources furthercomprises polling in real-time the plurality of sources to receive dataindicative of: real-time payload information for the respective flightsin the remaining flight sectors.
 16. The computing system of claim 15,wherein the operations further comprise: based on the real-timerespective weather conditions and the real-time payload information,calculating a quantity of fuel needed for the respective flights,wherein generating the linear model is further based on the quantity offuel needed for the respective flights.
 17. The computing system ofclaim 15, wherein polling in real-time the plurality of sources furthercomprises polling in real-time the plurality of sources to receive dataindicative of real-time logistical constraints at the remaining arrivalstations, and wherein the operations further comprise: based on (i) thereal-time respective weather conditions, (ii) the real-time logisticalconstraints at the remaining arrival stations and (iii) notice to airmen(NOTAM) constraints, determining a list of available of remainingarrival stations in the flight route, wherein the linear model isfurther based on the list of available of remaining arrival stations inthe flight rout.
 18. The computing system of claim 13, whereingenerating the linear model is further based on a plurality oflogistical constraints comprising: a maximum takeoff weight (MTOW) forthe aircraft, a maximum landing weight (MLW) for the aircraft, a maximumfuel capacity (MAXF) for the aircraft, a minimum fuel uplift quantityrequired for the respective flights, and a respective minimum quantityof reserve fuel for each arrival station.
 19. A non-transitory computerreadable storage medium having stored thereon program instructions thatwhen executed by a processor cause performance of a set of acts fordynamically calculating a total fuel uplift quantity for an aircraft toaccount for changing factors as the aircraft flies along a flight routethat comprises a plurality of flight sectors, wherein a flight sectorcomprises a respective flight between a respective departure station anda respective arrival station, the instructions comprising: (a) pollingin real-time a plurality of sources to receive data indicative of: (i)real-time respective weather conditions in each of one or more remainingflight sectors in the flight route, and (ii) respective delayinformation for the respective flights in the remaining flight sectors;(b) calculating for the remaining flight sectors a respective fuelconsumption factor, wherein the respective fuel consumption factor is afunction of altitude and travel distance that accounts for variations infuel consumption due to variations in altitude and travel distance ofthe aircraft; (c) based on (i) respective fuel quotations at theremaining arrival stations, (ii) the real-time weather conditions, and(iii) the respective delay information for the respective flights in theremaining flight sectors, generating a linear model for calculating arespective fuel uplift quantity at each remaining arrival station,wherein the respective fuel uplift quantity includes a respectivequantity of fuel with which the aircraft is refueled at each remainingarrival station; (d) calculating using the linear model the respectivefuel uplift quantity at each remaining arrival station, wherein thetotal fuel uplift quantity is a sum of the respective fuel upliftquantities at the arrival stations in the flight route; and (e)periodically performing operations (a)-(d) to update a calculation ofthe respective fuel uplift quantities.
 20. The non-transitory computerreadable storage medium of claim 19, wherein the linear model is linearprogramming model, wherein an objective function of the linearprogramming model is to minimize the total fuel uplift quantity, andwherein a decision variable of the linear programming model is therespective fuel uplift quantities.